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> ## Section 3.2.4.A of the pre-analysis plan: "Photo-Specific Measures
> ## (conditional on Likert-type or pairwise data) A. Distance between
> ## Posterior Mean Ranks"
> ##
> ## Kevin Quinn
> ## University of Michigan
> ##
> ## 7/15/2019
> ##
> 
> #library(TopKLists)
> library(coda)
> 
> set.seed(2102983)
> 
> 
> ########################################################################
> ## START a1-b1 comparisons
> ########################################################################
> 
> ## load MCMC output
> load("MM3.2.a1.M1.out.Rda")
> a1.M1.out <- a1.M1.out[sample(1:nrow(a1.M1.out), nrow(a1.M1.out),
+                               replace=FALSE), ]
> load("MM3.2.a1.M2.out.Rda")
> a1.M2.out <- a1.M2.out[sample(1:nrow(a1.M2.out), nrow(a1.M2.out),
+                               replace=FALSE), ]
> load("MM3.2.a1.pairwise.out.Rda")
> a1.pairwise.out <- a1.pairwise.out[sample(1:nrow(a1.pairwise.out),
+                                           nrow(a1.pairwise.out),
+                               replace=FALSE), ]
> 
> load("MM3.2.b1.M1.out.Rda")
> b1.M1.out <- b1.M1.out[sample(1:nrow(b1.M1.out), nrow(b1.M1.out),
+                               replace=FALSE), ]
> 
> load("MM3.2.b1.M2.out.Rda")
> b1.M2.out <- b1.M2.out[sample(1:nrow(b1.M2.out), nrow(b1.M2.out),
+                               replace=FALSE), ]
> load("MM3.2.b1.pairwise.out.Rda")
> b1.pairwise.out <- b1.pairwise.out[sample(1:nrow(b1.pairwise.out),
+                                           nrow(b1.pairwise.out),
+                               replace=FALSE), ]
> 
> ## make MCMC output comparable
> a1.M1.out <- a1.M1.out[,1:48]
> a1.M2.out <- a1.M2.out[,1:48]
> b1.M1.out <- b1.M1.out[,1:48]
> b1.M2.out <- b1.M2.out[,1:48]
> 
> colnames(a1.M1.out) <- gsub("as.factor\\(photoID\\)", "", colnames(a1.M1.out))
> colnames(a1.M2.out) <- gsub("as.factor\\(photoID\\)", "", colnames(a1.M2.out))
> colnames(b1.M1.out) <- gsub("as.factor\\(photoID\\)", "", colnames(b1.M1.out))
> colnames(b1.M2.out) <- gsub("as.factor\\(photoID\\)", "", colnames(b1.M2.out))
> colnames(a1.pairwise.out) <- gsub("theta\\.", "", colnames(a1.pairwise.out))
> colnames(b1.pairwise.out) <- gsub("theta\\.", "", colnames(b1.pairwise.out))
> 
> for (j in 2:48){
+     a1.M1.out[,j] <- a1.M1.out[,1] + a1.M1.out[,j]
+     a1.M2.out[,j] <- a1.M2.out[,1] + a1.M2.out[,j]
+     b1.M1.out[,j] <- b1.M1.out[,1] + b1.M1.out[,j]
+     b1.M2.out[,j] <- b1.M2.out[,1] + b1.M2.out[,j]
+ }
> colnames(a1.M1.out)[1] <- colnames(a1.pairwise.out)[1]
> colnames(a1.M2.out)[1] <- colnames(a1.pairwise.out)[1]
> colnames(b1.M1.out)[1] <- colnames(a1.pairwise.out)[1]
> colnames(b1.M2.out)[1] <- colnames(a1.pairwise.out)[1]
> 
> 
> M <- nrow(a1.M1.out)
> J <- ncol(a1.M1.out)
> 
> rank.a1.M1 <- matrix(NA, M, J)
> rank.a1.M2 <- matrix(NA, M, J)
> rank.b1.M1 <- matrix(NA, M, J)
> rank.b1.M2 <- matrix(NA, M, J)
> rank.a1.pair <- matrix(NA, M, J)
> rank.b1.pair <- matrix(NA, M, J)
> 
> for (iter in 1:M){
+     rank.a1.M1[iter,] <- rank(a1.M1.out[iter,])
+     rank.a1.M2[iter,] <- rank(a1.M2.out[iter,])
+     rank.b1.M1[iter,] <- rank(b1.M1.out[iter,])
+     rank.b1.M2[iter,] <- rank(b1.M2.out[iter,])
+     rank.a1.pair[iter,] <- rank(a1.pairwise.out[iter,])
+     rank.b1.pair[iter,] <- rank(b1.pairwise.out[iter,])
+ }
> 
> diff.a1b1.M1 <- colMeans(rank.a1.M1) - colMeans(rank.b1.M1)
> diff.a1b1.M2 <- colMeans(rank.a1.M2) - colMeans(rank.b1.M2)
> diff.a1b1.pair <- colMeans(rank.a1.pair) - colMeans(rank.b1.pair)
> ordvec <- order(diff.a1b1.pair)
> 
> pdf(file="MM3.2.4.A.a1b1.pdf", height=6, width=7)
> plot(diff.a1b1.M1[ordvec], xlab="Photo Index",
+      ylab="Difference in Expected Rank",
+      main="Coder Race Correlates with Photos Observed", pch=0,
+      col="orangered1", ylim=c(-17,17))
> abline(h=0)
> points(diff.a1b1.M1[ordvec], pch=0, col="orangered1")
> points(diff.a1b1.M2[ordvec], pch=1, col="dodgerblue")
> points(diff.a1b1.pair[ordvec], pch=3, col="purple4")
> legend(x=5, y=17, legend=c("M1", "M2", "Pairwise"), pch=c(0,1,3),
+        col=c("orangered1", "dodgerblue", "purple4"))
> dev.off()
null device 
          1 
> ########################################################################
> ## END a1-b1 comparisons
> ########################################################################
> 
> 
> 
> 
> 
> 
> ########################################################################
> ## START a2-b2 comparisons
> ########################################################################
> 
> ## load MCMC output
> load("MM3.2.a2.M1.out.Rda")
> a2.M1.out <- a2.M1.out[sample(1:nrow(a2.M1.out), nrow(a2.M1.out),
+                               replace=FALSE), ]
> load("MM3.2.a2.M2.out.Rda")
> a2.M2.out <- a2.M2.out[sample(1:nrow(a2.M2.out), nrow(a2.M2.out),
+                               replace=FALSE), ]
> load("MM3.2.a2.pairwise.out.Rda")
> a2.pairwise.out <- a2.pairwise.out[sample(1:nrow(a2.pairwise.out),
+                                           nrow(a2.pairwise.out),
+                               replace=FALSE), ]
> 
> load("MM3.2.b2.M1.out.Rda")
> b2.M1.out <- b2.M1.out[sample(1:nrow(b2.M1.out), nrow(b2.M1.out),
+                               replace=FALSE), ]
> 
> load("MM3.2.b2.M2.out.Rda")
> b2.M2.out <- b2.M2.out[sample(1:nrow(b2.M2.out), nrow(b2.M2.out),
+                               replace=FALSE), ]
> load("MM3.2.b2.pairwise.out.Rda")
> b2.pairwise.out <- b2.pairwise.out[sample(1:nrow(b2.pairwise.out),
+                                           nrow(b2.pairwise.out),
+                               replace=FALSE), ]
> 
> ## make MCMC output comparable
> a2.M1.out <- a2.M1.out[,1:48]
> a2.M2.out <- a2.M2.out[,1:48]
> b2.M1.out <- b2.M1.out[,1:48]
> b2.M2.out <- b2.M2.out[,1:48]
> 
> colnames(a2.M1.out) <- gsub("as.factor\\(photoID\\)", "", colnames(a2.M1.out))
> colnames(a2.M2.out) <- gsub("as.factor\\(photoID\\)", "", colnames(a2.M2.out))
> colnames(b2.M1.out) <- gsub("as.factor\\(photoID\\)", "", colnames(b2.M1.out))
> colnames(b2.M2.out) <- gsub("as.factor\\(photoID\\)", "", colnames(b2.M2.out))
> colnames(a2.pairwise.out) <- gsub("theta\\.", "", colnames(a2.pairwise.out))
> colnames(b2.pairwise.out) <- gsub("theta\\.", "", colnames(b2.pairwise.out))
> 
> for (j in 2:48){
+     a2.M1.out[,j] <- a2.M1.out[,1] + a2.M1.out[,j]
+     a2.M2.out[,j] <- a2.M2.out[,1] + a2.M2.out[,j]
+     b2.M1.out[,j] <- b2.M1.out[,1] + b2.M1.out[,j]
+     b2.M2.out[,j] <- b2.M2.out[,1] + b2.M2.out[,j]
+ }
> colnames(a2.M1.out)[1] <- colnames(a2.pairwise.out)[1]
> colnames(a2.M2.out)[1] <- colnames(a2.pairwise.out)[1]
> colnames(b2.M1.out)[1] <- colnames(a2.pairwise.out)[1]
> colnames(b2.M2.out)[1] <- colnames(a2.pairwise.out)[1]
> 
> 
> M <- nrow(a2.M1.out)
> J <- ncol(a1.M1.out)
> 
> rank.a2.M1 <- matrix(NA, M, J)
> rank.a2.M2 <- matrix(NA, M, J)
> rank.b2.M1 <- matrix(NA, M, J)
> rank.b2.M2 <- matrix(NA, M, J)
> rank.a2.pair <- matrix(NA, M, J)
> rank.b2.pair <- matrix(NA, M, J)
> 
> for (iter in 1:M){
+     rank.a2.M1[iter,] <- rank(a2.M1.out[iter,])
+     rank.a2.M2[iter,] <- rank(a2.M2.out[iter,])
+     rank.b2.M1[iter,] <- rank(b2.M1.out[iter,])
+     rank.b2.M2[iter,] <- rank(b2.M2.out[iter,])
+     rank.a2.pair[iter,] <- rank(a2.pairwise.out[iter,])
+     rank.b2.pair[iter,] <- rank(b2.pairwise.out[iter,])
+ }
> 
> diff.a2b2.M1 <- colMeans(rank.a2.M1) - colMeans(rank.b2.M1)
> diff.a2b2.M2 <- colMeans(rank.a2.M2) - colMeans(rank.b2.M2)
> diff.a2b2.pair <- colMeans(rank.a2.pair) - colMeans(rank.b2.pair)
> ordvec <- order(diff.a2b2.pair)
> 
> pdf(file="MM3.2.4.A.a2b2.pdf", height=6, width=7)
> plot(diff.a2b2.M1[ordvec], xlab="Photo Index",
+      ylab="Difference in Expected Rank",
+      main="Distractor-Photo Race Correlates with Photos Observed", pch=0,
+      col="orangered1", ylim=c(-17,17))
> abline(h=0)
> points(diff.a2b2.M1[ordvec], pch=0, col="orangered1")
> points(diff.a2b2.M2[ordvec], pch=1, col="dodgerblue")
> points(diff.a2b2.pair[ordvec], pch=3, col="purple4")
> legend(x=5, y=17, legend=c("M1", "M2", "Pairwise"), pch=c(0,1,3),
+        col=c("orangered1", "dodgerblue", "purple4"))
> dev.off()
null device 
          1 
> 
> 
> 
> 
> ########################################################################
> ## END a2-b2 comparisons
> ########################################################################
> 
> 
> 
> 
> 
> 
> 
> ########################################################################
> ## START a3-b3 comparisons
> ########################################################################
> 
> ## load MCMC output
> load("MM3.2.a3.M1.out.Rda")
> a3.M1.out <- a3.M1.out[sample(1:nrow(a3.M1.out), nrow(a3.M1.out),
+                               replace=FALSE), ]
> load("MM3.2.a3.M2.out.Rda")
> a3.M2.out <- a3.M2.out[sample(1:nrow(a3.M2.out), nrow(a3.M2.out),
+                               replace=FALSE), ]
> load("MM3.2.a3.pairwise.out.Rda")
> a3.pairwise.out <- a3.pairwise.out[sample(1:nrow(a3.pairwise.out),
+                                           nrow(a3.pairwise.out),
+                               replace=FALSE), ]
> 
> load("MM3.2.b3.M1.out.Rda")
> b3.M1.out <- b3.M1.out[sample(1:nrow(b3.M1.out), nrow(b3.M1.out),
+                               replace=FALSE), ]
> 
> load("MM3.2.b3.M2.out.Rda")
> b3.M2.out <- b3.M2.out[sample(1:nrow(b3.M2.out), nrow(b3.M2.out),
+                               replace=FALSE), ]
> load("MM3.2.b3.pairwise.out.Rda")
> b3.pairwise.out <- b3.pairwise.out[sample(1:nrow(b3.pairwise.out),
+                                           nrow(b3.pairwise.out),
+                               replace=FALSE), ]
> 
> ## make MCMC output comparable
> a3.M1.out <- a3.M1.out[,1:48]
> a3.M2.out <- a3.M2.out[,1:48]
> b3.M1.out <- b3.M1.out[,1:48]
> b3.M2.out <- b3.M2.out[,1:48]
> 
> colnames(a3.M1.out) <- gsub("as.factor\\(photoID\\)", "", colnames(a3.M1.out))
> colnames(a3.M2.out) <- gsub("as.factor\\(photoID\\)", "", colnames(a3.M2.out))
> colnames(b3.M1.out) <- gsub("as.factor\\(photoID\\)", "", colnames(b3.M1.out))
> colnames(b3.M2.out) <- gsub("as.factor\\(photoID\\)", "", colnames(b3.M2.out))
> colnames(a3.pairwise.out) <- gsub("theta\\.", "", colnames(a3.pairwise.out))
> colnames(b3.pairwise.out) <- gsub("theta\\.", "", colnames(b3.pairwise.out))
> 
> for (j in 2:48){
+     a3.M1.out[,j] <- a3.M1.out[,1] + a3.M1.out[,j]
+     a3.M2.out[,j] <- a3.M2.out[,1] + a3.M2.out[,j]
+     b3.M1.out[,j] <- b3.M1.out[,1] + b3.M1.out[,j]
+     b3.M2.out[,j] <- b3.M2.out[,1] + b3.M2.out[,j]
+ }
> colnames(a3.M1.out)[1] <- colnames(a3.pairwise.out)[1]
> colnames(a3.M2.out)[1] <- colnames(a3.pairwise.out)[1]
> colnames(b3.M1.out)[1] <- colnames(a3.pairwise.out)[1]
> colnames(b3.M2.out)[1] <- colnames(a3.pairwise.out)[1]
> 
> 
> M <- nrow(a3.M1.out)
> J <- ncol(a1.M1.out)
> 
> rank.a3.M1 <- matrix(NA, M, J)
> rank.a3.M2 <- matrix(NA, M, J)
> rank.b3.M1 <- matrix(NA, M, J)
> rank.b3.M2 <- matrix(NA, M, J)
> rank.a3.pair <- matrix(NA, M, J)
> rank.b3.pair <- matrix(NA, M, J)
> 
> for (iter in 1:M){
+     rank.a3.M1[iter,] <- rank(a3.M1.out[iter,])
+     rank.a3.M2[iter,] <- rank(a3.M2.out[iter,])
+     rank.b3.M1[iter,] <- rank(b3.M1.out[iter,])
+     rank.b3.M2[iter,] <- rank(b3.M2.out[iter,])
+     rank.a3.pair[iter,] <- rank(a3.pairwise.out[iter,])
+     rank.b3.pair[iter,] <- rank(b3.pairwise.out[iter,])
+ }
> 
> diff.a3b3.M1 <- colMeans(rank.a3.M1) - colMeans(rank.b3.M1)
> diff.a3b3.M2 <- colMeans(rank.a3.M2) - colMeans(rank.b3.M2)
> diff.a3b3.pair <- colMeans(rank.a3.pair) - colMeans(rank.b3.pair)
> ordvec <- order(diff.a3b3.pair)
> 
> pdf(file="MM3.2.4.A.a3b3.pdf", height=6, width=7)
> plot(diff.a3b3.M1[ordvec], xlab="Photo Index",
+      ylab="Difference in Expected Rank",
+      main="Black vs. White Coders", pch=0,
+      col="orangered1", ylim=c(-17,17))
> abline(h=0)
> points(diff.a3b3.M1[ordvec], pch=0, col="orangered1")
> points(diff.a3b3.M2[ordvec], pch=1, col="dodgerblue")
> points(diff.a3b3.pair[ordvec], pch=3, col="purple4")
> legend(x=5, y=17, legend=c("M1", "M2", "Pairwise"), pch=c(0,1,3),
+        col=c("orangered1", "dodgerblue", "purple4"))
> dev.off()
null device 
          1 
> 
> 
> 
> 
> ########################################################################
> ## END a3-b3 comparisons
> ########################################################################
> 
> 
> 
> 
> 
> ########################################################################
> ## START a4-b4 comparisons
> ########################################################################
> 
> ## load MCMC output
> load("MM3.2.a4.M1.out.Rda")
> a4.M1.out <- a4.M1.out[sample(1:nrow(a4.M1.out), nrow(a4.M1.out),
+                               replace=FALSE), ]
> load("MM3.2.a4.M2.out.Rda")
> a4.M2.out <- a4.M2.out[sample(1:nrow(a4.M2.out), nrow(a4.M2.out),
+                               replace=FALSE), ]
> load("MM3.2.a4.pairwise.out.Rda")
> a4.pairwise.out <- a4.pairwise.out[sample(1:nrow(a4.pairwise.out),
+                                           nrow(a4.pairwise.out),
+                               replace=FALSE), ]
> 
> load("MM3.2.b4.M1.out.Rda")
> b4.M1.out <- b4.M1.out[sample(1:nrow(b4.M1.out), nrow(b4.M1.out),
+                               replace=FALSE), ]
> 
> load("MM3.2.b4.M2.out.Rda")
> b4.M2.out <- b4.M2.out[sample(1:nrow(b4.M2.out), nrow(b4.M2.out),
+                               replace=FALSE), ]
> load("MM3.2.b4.pairwise.out.Rda")
> b4.pairwise.out <- b4.pairwise.out[sample(1:nrow(b4.pairwise.out),
+                                           nrow(b4.pairwise.out),
+                               replace=FALSE), ]
> 
> ## make MCMC output comparable
> a4.M1.out <- a4.M1.out[,1:48]
> a4.M2.out <- a4.M2.out[,1:48]
> b4.M1.out <- b4.M1.out[,1:48]
> b4.M2.out <- b4.M2.out[,1:48]
> 
> colnames(a4.M1.out) <- gsub("as.factor\\(photoID\\)", "", colnames(a4.M1.out))
> colnames(a4.M2.out) <- gsub("as.factor\\(photoID\\)", "", colnames(a4.M2.out))
> colnames(b4.M1.out) <- gsub("as.factor\\(photoID\\)", "", colnames(b4.M1.out))
> colnames(b4.M2.out) <- gsub("as.factor\\(photoID\\)", "", colnames(b4.M2.out))
> colnames(a4.pairwise.out) <- gsub("theta\\.", "", colnames(a4.pairwise.out))
> colnames(b4.pairwise.out) <- gsub("theta\\.", "", colnames(b4.pairwise.out))
> 
> for (j in 2:48){
+     a4.M1.out[,j] <- a4.M1.out[,1] + a4.M1.out[,j]
+     a4.M2.out[,j] <- a4.M2.out[,1] + a4.M2.out[,j]
+     b4.M1.out[,j] <- b4.M1.out[,1] + b4.M1.out[,j]
+     b4.M2.out[,j] <- b4.M2.out[,1] + b4.M2.out[,j]
+ }
> colnames(a4.M1.out)[1] <- colnames(a4.pairwise.out)[1]
> colnames(a4.M2.out)[1] <- colnames(a4.pairwise.out)[1]
> colnames(b4.M1.out)[1] <- colnames(a4.pairwise.out)[1]
> colnames(b4.M2.out)[1] <- colnames(a4.pairwise.out)[1]
> 
> 
> M <- nrow(a4.M1.out)
> J <- ncol(a1.M1.out)
> 
> rank.a4.M1 <- matrix(NA, M, J)
> rank.a4.M2 <- matrix(NA, M, J)
> rank.b4.M1 <- matrix(NA, M, J)
> rank.b4.M2 <- matrix(NA, M, J)
> rank.a4.pair <- matrix(NA, M, J)
> rank.b4.pair <- matrix(NA, M, J)
> 
> for (iter in 1:M){
+     rank.a4.M1[iter,] <- rank(a4.M1.out[iter,])
+     rank.a4.M2[iter,] <- rank(a4.M2.out[iter,])
+     rank.b4.M1[iter,] <- rank(b4.M1.out[iter,])
+     rank.b4.M2[iter,] <- rank(b4.M2.out[iter,])
+     rank.a4.pair[iter,] <- rank(a4.pairwise.out[iter,])
+     rank.b4.pair[iter,] <- rank(b4.pairwise.out[iter,])
+ }
> 
> diff.a4b4.M1 <- colMeans(rank.a4.M1) - colMeans(rank.b4.M1)
> diff.a4b4.M2 <- colMeans(rank.a4.M2) - colMeans(rank.b4.M2)
> diff.a4b4.pair <- colMeans(rank.a4.pair) - colMeans(rank.b4.pair)
> ordvec <- order(diff.a4b4.pair)
> 
> pdf(file="MM3.2.4.A.a4b4.pdf", height=6, width=7)
> plot(diff.a4b4.M1[ordvec], xlab="Photo Index",
+      ylab="Difference in Expected Rank",
+      main="Black vs. White Distractor Photos", pch=0,
+      col="orangered1", ylim=c(-17,17))
> abline(h=0)
> points(diff.a4b4.M1[ordvec], pch=0, col="orangered1")
> points(diff.a4b4.M2[ordvec], pch=1, col="dodgerblue")
> points(diff.a4b4.pair[ordvec], pch=3, col="purple4")
> legend(x=5, y=17, legend=c("M1", "M2", "Pairwise"), pch=c(0,1,3),
+        col=c("orangered1", "dodgerblue", "purple4"))
> dev.off()
null device 
          1 
> 
> 
> 
> 
> ########################################################################
> ## END a4-b4 comparisons
> ########################################################################
> 
> 
> proc.time()
   user  system elapsed 
 10.137   0.638  10.819 
